Connection Density Enhancement of Backscatter Communication Systems with Relaying
Bibliographic record
Abstract
Backscatter communication is a promising technology for energy-efficient communications. It enables the Internet of things (IoT) devices to send their data by backscattering and modulating the incident radio frequency (RF) signals. In this paper, we propose a scheme for improving the connection density of backscatter communication systems, i.e., increasing the number of backscattering-enabled IoT devices that meet a minimum threshold of the received signal-to-noise ratio (SNR) at the serving base station (BS). The aforementioned goal is achieved by allowing the user equipment (UE) devices to relay the backscattered signals from the IoT devices. A UE superimposes its own uplink data with the data from an associated IoT device using power-domain non-orthogonal multiple access (NOMA). Since the UEs are mobile and have higher transmit power, the IoT devices utilize the nearby UEs to relay their data. In addition, using UEs as relays helps the BS to support more backscattering-enabled IoT devices. We formulate the connection density maximization problem to pair the IoT devices with the available UE relays. The formulated problem is a mixed-integer linear programming (MILP) problem. Although the formulated problem can be solved optimally, it has an exponential complexity. Hence, we propose a suboptimal algorithm which decomposes the original problem into smaller subproblems that can be solved by low complexity algorithms. Simulation results show that the proposed scheme with UEs as relays can increase the connection density by up to 65% compared to deploying fixed relays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".